Advancing data honesty in experimental biology

The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to be a widespread problem across the academic world, where published results are the major currency, incentivizing publication of (usually positive) results at the cost of lax scientific rigor and even fraudulent data. Although solutions to improve data sharing and methodological transparency are increasingly being implemented, the inability to detect dishonesty within raw data remains an inherent flaw in the way in which we judge research. We therefore propose that one solution would be the development of a non-modifiable raw data format that could be published alongside scientific results; a format that would enable data authentication from the earliest stages of experimental data collection. A further extension of this tool could allow changes to the initial original version to be tracked, so every reviewer and reader could follow the logical footsteps of the author and detect unintentional errors or intentional manipulations of the data. Were such a tool to be developed, we would not advocate its use as a prerequisite for journal submission; rather, we envisage that authors would be given the option to provide such authentication. Only authors who did not manipulate or fabricate their data can provide the original data without risking discovery, so the mere choice to do so already increases their credibility (much like 'honest signaling' in animals). We strongly believe that such a tool would enhance data honesty and encourage more reliable science.

Manipulating and Basic Analysis of Tabular Metagenomics Datasets Using R

Handling and manipulating tabular datasets is a critical step in every metagenomics analysis pipeline. The R statistical programming language offers a variety of versatile tools for working with tabular data that allow for the development of computationally efficient and reproducible workflows. Here we outline the basics of the R programming language and showcase a number of tools for data manipulation and basic analysis of metagenomics datasets.

Streamlining urban forest monitoring based on a large-scale tree survey: a case study of highway vegetation in Hong Kong

Through the analysis of an urban tree inventory with the aid of machine learning, this study brought together different aspects of urban forestry. Urban tree monitoring is essential to successful urban forestry. Transport land use accommodates huge tree stock which requires substantial monitoring efforts. In Hong Kong, more research is needed to take into consideration how monitoring works can be improved in response to variations in tree stand characteristics. This case study aimed to illustrate the usefulness of a large-scale tree survey in mainstreaming future tree monitoring and management in transport land use. A total of 7209 trees were found in a large-scale tree survey conducted in 53 slopes and 52 verges along San Tin Highway in Hong Kong. Dominance by Corymbia citriodora (72%) was observed, especially for the highway verges. Using chi-square tests, significant associations were found between monospecific stands, habitat type, and tree risk rating. A logistic regression model was constructed to predict the occurrence of monoculture. Every metre increase in maximum tree height, the odds of a stand being monospecific would be 1.22 times greater. Stands on verges had 5.26 times greater odds of being monospecific against the slope. The associations and relationships were attributed to the dominance of C. citriodora. By boosting the logistic model, model reliability increased as kappa rose from 0.51 to 0.63, while balanced accuracy improved from 0.72 to 0.85. The occurrence of monospecific stands could be reliably predicted using maximum tree height and habitat type of tree stands. These quantitative findings monitoring can guide urban forest monitoring. Through a better understanding of urban forest structure and composition, future monitoring can aid the mainstreaming of urban forestry in transport planning.

Political regime, data transparency, and COVID-19 death cases

The COVID-19-the worst pandemic since the Spanish flu-has dramatically changed the world, with a significant number of people suffering from and dying of the disease. Some scholars argue that democratic governments are disadvantaged in coping with the current pandemic mainly because they cannot intervene in their citizens' lives as aggressively as their authoritarian counterparts. Other scholars, however, suggest that possible data manipulation may account for the apparent advantage of authoritarian countries. Taking such a possibility seriously, this paper analyzes the relationship between political regimes, data transparency, and COVID-19 deaths using cross-national data for over 108 countries, obtained from Worldometer COVID-19 Data, Polity V Project, Variety of Democracy (V-Dem) Project, HRV Transparency Project among other sources. Regression analyses indicate that authoritarian countries do not necessarily tend to have fewer COVID-19 deaths than their democratic counterparts after controlling for other factors, especially data transparency. The transparency variable itself, on the other hand, is positively correlated with the number of death cases more consistently (P <0.05). Overall, the estimation results point to the possible data manipulation, not the nature of regime characteristics itself, as a more significant source for the seemingly low casualty rates in authoritarian countries.